Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells420
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric17
Unsupported5
Categorical1

Alerts

Natural gas has 30 (57.7%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 35 (67.3%) missing valuesMissing
Firewood has 52 (100.0%) missing valuesMissing
Sugarcane and products has 52 (100.0%) missing valuesMissing
Other Primary_x000d_ has 45 (86.5%) missing valuesMissing
Electricity has 52 (100.0%) missing valuesMissing
Kerosene/jet fuel has 1 (1.9%) missing valuesMissing
Coke has 1 (1.9%) missing valuesMissing
Charcoal has 52 (100.0%) missing valuesMissing
Gases has 48 (92.3%) missing valuesMissing
Year is uniformly distributedUniform
Year has unique valuesUnique
Oil has unique valuesUnique
Coal has unique valuesUnique
Total Primaries has unique valuesUnique
LPG has unique valuesUnique
Gasoline/alcohol has unique valuesUnique
Diesel oil has unique valuesUnique
Fuel oil has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Firewood is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sugarcane and products is an unsupported type, check if it needs cleaning or further analysisUnsupported
Electricity is an unsupported type, check if it needs cleaning or further analysisUnsupported
Charcoal is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-07-30 07:30:45.648304
Analysis finished2023-07-30 07:31:56.495395
Duration1 minute and 10.85 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:31:56.651307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:31:56.927075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.30327
Minimum-3821.01
Maximum3066.78
Zeros0
Zeros (%)0.0%
Negative23
Negative (%)44.2%
Memory size548.0 B
2023-07-30T07:31:57.220423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3821.01
5-th percentile-1993.831
Q1-510.8275
median208.575
Q3935.675
95-th percentile2235.7955
Maximum3066.78
Range6887.79
Interquartile range (IQR)1446.5025

Descriptive statistics

Standard deviation1331.4993
Coefficient of variation (CV)11.448511
Kurtosis0.68628071
Mean116.30327
Median Absolute Deviation (MAD)721.54
Skewness-0.32924231
Sum6047.77
Variance1772890.3
MonotonicityNot monotonic
2023-07-30T07:31:57.474037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-277.33 1
 
1.9%
-966.96 1
 
1.9%
-240.21 1
 
1.9%
284.97 1
 
1.9%
-1285.15 1
 
1.9%
2390.16 1
 
1.9%
923.32 1
 
1.9%
324.12 1
 
1.9%
1056.95 1
 
1.9%
309.48 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-3821.01 1
1.9%
-2503.7 1
1.9%
-2116.8 1
1.9%
-1893.22 1
1.9%
-1744.95 1
1.9%
-1573.38 1
1.9%
-1543.43 1
1.9%
-1285.15 1
1.9%
-1007.78 1
1.9%
-966.96 1
1.9%
ValueCountFrequency (%)
3066.78 1
1.9%
2390.16 1
1.9%
2326.76 1
1.9%
2161.37 1
1.9%
2121.62 1
1.9%
2046.85 1
1.9%
1491.75 1
1.9%
1456 1
1.9%
1302.42 1
1.9%
1115.55 1
1.9%

Natural gas
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing30
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean-1415.9418
Minimum-3618.96
Maximum169.49
Zeros0
Zeros (%)0.0%
Negative17
Negative (%)32.7%
Memory size548.0 B
2023-07-30T07:31:57.706126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3618.96
5-th percentile-3553.637
Q1-2175.46
median-1316.835
Q3-173.725
95-th percentile17.3735
Maximum169.49
Range3788.45
Interquartile range (IQR)2001.735

Descriptive statistics

Standard deviation1175.5039
Coefficient of variation (CV)-0.83019226
Kurtosis-0.80160554
Mean-1415.9418
Median Absolute Deviation (MAD)1071.645
Skewness-0.27735145
Sum-31150.72
Variance1381809.5
MonotonicityNot monotonic
2023-07-30T07:31:57.912813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4.79 2
 
3.8%
-1933.24 1
 
1.9%
-3618.96 1
 
1.9%
-1840.16 1
 
1.9%
-2256.2 1
 
1.9%
-2675.45 1
 
1.9%
-2693.07 1
 
1.9%
-2520.76 1
 
1.9%
-1209.1 1
 
1.9%
-1424.57 1
 
1.9%
Other values (11) 11
 
21.2%
(Missing) 30
57.7%
ValueCountFrequency (%)
-3618.96 1
1.9%
-3598.93 1
1.9%
-2693.07 1
1.9%
-2675.45 1
1.9%
-2520.76 1
1.9%
-2256.2 1
1.9%
-1933.24 1
1.9%
-1912.82 1
1.9%
-1840.16 1
1.9%
-1660.47 1
1.9%
ValueCountFrequency (%)
169.49 1
1.9%
17.9 1
1.9%
7.37 1
1.9%
4.79 2
3.8%
-2.87 1
1.9%
-686.29 1
1.9%
-1029.44 1
1.9%
-1115.23 1
1.9%
-1177.5 1
1.9%
-1209.1 1
1.9%

Coal
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.3611538
Minimum-642.78
Maximum638
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)50.0%
Memory size548.0 B
2023-07-30T07:31:58.163695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-642.78
5-th percentile-396.9865
Q1-218.8225
median-16.48
Q3161.9525
95-th percentile543.855
Maximum638
Range1280.78
Interquartile range (IQR)380.775

Descriptive statistics

Standard deviation300.80641
Coefficient of variation (CV)-89.494985
Kurtosis-0.3062935
Mean-3.3611538
Median Absolute Deviation (MAD)200.185
Skewness0.37531742
Sum-174.78
Variance90484.499
MonotonicityNot monotonic
2023-07-30T07:31:58.415766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-167.52 1
 
1.9%
30.12 1
 
1.9%
-276 1
 
1.9%
472.24 1
 
1.9%
155.12 1
 
1.9%
602.54 1
 
1.9%
-171.74 1
 
1.9%
-38.19 1
 
1.9%
196.4 1
 
1.9%
42.82 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-642.78 1
1.9%
-506.44 1
1.9%
-412.92 1
1.9%
-383.95 1
1.9%
-379.73 1
1.9%
-371.53 1
1.9%
-306.57 1
1.9%
-289.86 1
1.9%
-280.31 1
1.9%
-279.55 1
1.9%
ValueCountFrequency (%)
638 1
1.9%
619.15 1
1.9%
602.54 1
1.9%
495.84 1
1.9%
486.95 1
1.9%
472.24 1
1.9%
420.33 1
1.9%
332.83 1
1.9%
325.44 1
1.9%
303.8 1
1.9%

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Real number (ℝ)

Distinct17
Distinct (%)100.0%
Missing35
Missing (%)67.3%
Infinite0
Infinite (%)0.0%
Mean-82.159412
Minimum-708.26
Maximum137.57
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)15.4%
Memory size548.0 B
2023-07-30T07:31:58.634578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-708.26
5-th percentile-571.308
Q1-86.75
median4.28
Q368.81
95-th percentile112.354
Maximum137.57
Range845.83
Interquartile range (IQR)155.56

Descriptive statistics

Standard deviation242.06468
Coefficient of variation (CV)-2.9462806
Kurtosis2.0418419
Mean-82.159412
Median Absolute Deviation (MAD)85.87
Skewness-1.682929
Sum-1396.71
Variance58595.311
MonotonicityNot monotonic
2023-07-30T07:31:58.811510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-537.07 1
 
1.9%
-409.82 1
 
1.9%
-708.26 1
 
1.9%
90.15 1
 
1.9%
16.17 1
 
1.9%
-153.09 1
 
1.9%
-33.99 1
 
1.9%
39.98 1
 
1.9%
-86.75 1
 
1.9%
68.81 1
 
1.9%
Other values (7) 7
 
13.5%
(Missing) 35
67.3%
ValueCountFrequency (%)
-708.26 1
1.9%
-537.07 1
1.9%
-409.82 1
1.9%
-153.09 1
1.9%
-86.75 1
1.9%
-62.36 1
1.9%
-33.99 1
1.9%
-22.29 1
1.9%
4.28 1
1.9%
16.17 1
1.9%
ValueCountFrequency (%)
137.57 1
1.9%
106.05 1
1.9%
99.2 1
1.9%
90.15 1
1.9%
68.81 1
1.9%
54.71 1
1.9%
39.98 1
1.9%
16.17 1
1.9%
4.28 1
1.9%
-22.29 1
1.9%

Firewood
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Sugarcane and products
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Other Primary_x000d_
Real number (ℝ)

Distinct7
Distinct (%)100.0%
Missing45
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean-26.352857
Minimum-106.75
Maximum30.68
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)9.6%
Memory size548.0 B
2023-07-30T07:31:58.981537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-106.75
5-th percentile-89.887
Q1-39.14
median-24.23
Q3-2.945
95-th percentile23.912
Maximum30.68
Range137.43
Interquartile range (IQR)36.195

Descriptive statistics

Standard deviation44.061737
Coefficient of variation (CV)-1.6719909
Kurtosis1.302836
Mean-26.352857
Median Absolute Deviation (MAD)26.31
Skewness-0.83315465
Sum-184.47
Variance1941.4367
MonotonicityNot monotonic
2023-07-30T07:31:59.149477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-14.01 1
 
1.9%
-50.54 1
 
1.9%
-106.75 1
 
1.9%
30.68 1
 
1.9%
-27.74 1
 
1.9%
-24.23 1
 
1.9%
8.12 1
 
1.9%
(Missing) 45
86.5%
ValueCountFrequency (%)
-106.75 1
1.9%
-50.54 1
1.9%
-27.74 1
1.9%
-24.23 1
1.9%
-14.01 1
1.9%
8.12 1
1.9%
30.68 1
1.9%
ValueCountFrequency (%)
30.68 1
1.9%
8.12 1
1.9%
-14.01 1
1.9%
-24.23 1
1.9%
-27.74 1
1.9%
-50.54 1
1.9%
-106.75 1
1.9%

Total Primaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-516.51808
Minimum-4538.89
Maximum2992.7
Zeros0
Zeros (%)0.0%
Negative34
Negative (%)65.4%
Memory size548.0 B
2023-07-30T07:31:59.396890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4538.89
5-th percentile-3076.3775
Q1-1321.3375
median-389.105
Q3375.38
95-th percentile2127.116
Maximum2992.7
Range7531.59
Interquartile range (IQR)1696.7175

Descriptive statistics

Standard deviation1552.7748
Coefficient of variation (CV)-3.0062351
Kurtosis0.42620399
Mean-516.51808
Median Absolute Deviation (MAD)791.42
Skewness-0.15849232
Sum-26858.94
Variance2411109.5
MonotonicityNot monotonic
2023-07-30T07:31:59.644554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-444.85 1
 
1.9%
-936.84 1
 
1.9%
-926.02 1
 
1.9%
894.77 1
 
1.9%
-1130.03 1
 
1.9%
2992.7 1
 
1.9%
-277.86 1
 
1.9%
-400.36 1
 
1.9%
138.12 1
 
1.9%
-825.2 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-4538.89 1
1.9%
-3819.84 1
1.9%
-3401.62 1
1.9%
-2810.27 1
1.9%
-2614.59 1
1.9%
-2496.54 1
1.9%
-2172.77 1
1.9%
-2087 1
1.9%
-1980.51 1
1.9%
-1847.61 1
1.9%
ValueCountFrequency (%)
2992.7 1
1.9%
2614.32 1
1.9%
2400.18 1
1.9%
1903.7 1
1.9%
1710.84 1
1.9%
1486.79 1
1.9%
923.49 1
1.9%
894.77 1
1.9%
609.27 1
1.9%
537.24 1
1.9%

Electricity
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

LPG
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-61.489038
Minimum-395.41
Maximum91.02
Zeros0
Zeros (%)0.0%
Negative38
Negative (%)73.1%
Memory size548.0 B
2023-07-30T07:31:59.893658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-395.41
5-th percentile-246.6725
Q1-105.735
median-59.885
Q34.4775
95-th percentile58.9355
Maximum91.02
Range486.43
Interquartile range (IQR)110.2125

Descriptive statistics

Standard deviation97.052798
Coefficient of variation (CV)-1.5783756
Kurtosis1.8800976
Mean-61.489038
Median Absolute Deviation (MAD)58.965
Skewness-1.1219302
Sum-3197.43
Variance9419.2457
MonotonicityNot monotonic
2023-07-30T07:32:00.165529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-97.67 1
 
1.9%
-104.43 1
 
1.9%
61.57 1
 
1.9%
-15.52 1
 
1.9%
-105.96 1
 
1.9%
48.72 1
 
1.9%
-0.06 1
 
1.9%
91.02 1
 
1.9%
89.6 1
 
1.9%
56.78 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-395.41 1
1.9%
-284.61 1
1.9%
-267.16 1
1.9%
-229.91 1
1.9%
-188.41 1
1.9%
-174.46 1
1.9%
-167.52 1
1.9%
-157.3 1
1.9%
-129 1
1.9%
-123.3 1
1.9%
ValueCountFrequency (%)
91.02 1
1.9%
89.6 1
1.9%
61.57 1
1.9%
56.78 1
1.9%
48.72 1
1.9%
45.6 1
1.9%
34.45 1
1.9%
24.6 1
1.9%
21.6 1
1.9%
19.26 1
1.9%

Gasoline/alcohol
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.7861538
Minimum-954.79
Maximum998.87
Zeros0
Zeros (%)0.0%
Negative28
Negative (%)53.8%
Memory size548.0 B
2023-07-30T07:32:00.427750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-954.79
5-th percentile-771.6855
Q1-352.6375
median-64.6
Q3325.47
95-th percentile865.174
Maximum998.87
Range1953.66
Interquartile range (IQR)678.1075

Descriptive statistics

Standard deviation519.09001
Coefficient of variation (CV)-137.1022
Kurtosis-0.79140079
Mean-3.7861538
Median Absolute Deviation (MAD)360.1
Skewness0.20677104
Sum-196.88
Variance269454.44
MonotonicityNot monotonic
2023-07-30T07:32:00.680471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.35 1
 
1.9%
140.25 1
 
1.9%
271.12 1
 
1.9%
591.78 1
 
1.9%
769.47 1
 
1.9%
226.82 1
 
1.9%
589.5 1
 
1.9%
-614.85 1
 
1.9%
316.8 1
 
1.9%
351.48 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-954.79 1
1.9%
-876.29 1
1.9%
-775.42 1
1.9%
-768.63 1
1.9%
-766.38 1
1.9%
-657.02 1
1.9%
-614.85 1
1.9%
-581.45 1
1.9%
-562.68 1
1.9%
-526.65 1
1.9%
ValueCountFrequency (%)
998.87 1
1.9%
959.87 1
1.9%
926.18 1
1.9%
815.26 1
1.9%
769.47 1
1.9%
745.34 1
1.9%
736.08 1
1.9%
661 1
1.9%
591.78 1
1.9%
589.5 1
1.9%

Kerosene/jet fuel
Real number (ℝ)

Distinct50
Distinct (%)98.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-43.454902
Minimum-381.23
Maximum204.97
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)61.5%
Memory size548.0 B
2023-07-30T07:32:00.943087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-381.23
5-th percentile-228.19
Q1-77.59
median-30.25
Q322.005
95-th percentile66.77
Maximum204.97
Range586.2
Interquartile range (IQR)99.595

Descriptive statistics

Standard deviation98.936937
Coefficient of variation (CV)-2.2767728
Kurtosis2.5585422
Mean-43.454902
Median Absolute Deviation (MAD)51.47
Skewness-1.0102713
Sum-2216.2
Variance9788.5175
MonotonicityNot monotonic
2023-07-30T07:32:01.197139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-54.86 2
 
3.8%
-42.15 1
 
1.9%
-53.13 1
 
1.9%
-12.86 1
 
1.9%
29.32 1
 
1.9%
-30.25 1
 
1.9%
3.28 1
 
1.9%
59.67 1
 
1.9%
-14.76 1
 
1.9%
-5.49 1
 
1.9%
Other values (40) 40
76.9%
ValueCountFrequency (%)
-381.23 1
1.9%
-287.11 1
1.9%
-228.94 1
1.9%
-227.44 1
1.9%
-172.44 1
1.9%
-170.47 1
1.9%
-158.22 1
1.9%
-124.52 1
1.9%
-108.89 1
1.9%
-88.15 1
1.9%
ValueCountFrequency (%)
204.97 1
1.9%
78.2 1
1.9%
68.78 1
1.9%
64.76 1
1.9%
59.67 1
1.9%
48.49 1
1.9%
32.89 1
1.9%
30.18 1
1.9%
29.32 1
1.9%
26.95 1
1.9%

Diesel oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.104231
Minimum-697.58
Maximum482.76
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)61.5%
Memory size548.0 B
2023-07-30T07:32:01.465637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-697.58
5-th percentile-262.728
Q1-170.6375
median-40.4
Q351.43
95-th percentile444.331
Maximum482.76
Range1180.34
Interquartile range (IQR)222.0675

Descriptive statistics

Standard deviation222.86026
Coefficient of variation (CV)-10.082245
Kurtosis1.3458006
Mean-22.104231
Median Absolute Deviation (MAD)121.51
Skewness0.33241631
Sum-1149.42
Variance49666.696
MonotonicityNot monotonic
2023-07-30T07:32:01.724875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-42.01 1
 
1.9%
14.57 1
 
1.9%
-59.34 1
 
1.9%
-149.28 1
 
1.9%
-232.46 1
 
1.9%
10.5 1
 
1.9%
452.79 1
 
1.9%
-154.03 1
 
1.9%
-249.56 1
 
1.9%
437.41 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-697.58 1
1.9%
-314.64 1
1.9%
-267.48 1
1.9%
-258.84 1
1.9%
-249.56 1
1.9%
-232.46 1
1.9%
-226.16 1
1.9%
-212.78 1
1.9%
-193.88 1
1.9%
-190.89 1
1.9%
ValueCountFrequency (%)
482.76 1
1.9%
464.12 1
1.9%
452.79 1
1.9%
437.41 1
1.9%
379.79 1
1.9%
319.18 1
1.9%
312.98 1
1.9%
150.86 1
1.9%
149.89 1
1.9%
148.79 1
1.9%

Fuel oil
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-29.270962
Minimum-3130.1
Maximum1001.46
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)51.9%
Memory size548.0 B
2023-07-30T07:32:02.037774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3130.1
5-th percentile-444.625
Q1-78.08
median-5.61
Q3136.295
95-th percentile389.306
Maximum1001.46
Range4131.56
Interquartile range (IQR)214.375

Descriptive statistics

Standard deviation522.54241
Coefficient of variation (CV)-17.851905
Kurtosis24.830764
Mean-29.270962
Median Absolute Deviation (MAD)113.325
Skewness-4.0392032
Sum-1522.09
Variance273050.57
MonotonicityNot monotonic
2023-07-30T07:32:02.400575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-69.03 1
 
1.9%
-210.79 1
 
1.9%
-138.01 1
 
1.9%
112.51 1
 
1.9%
-224.1 1
 
1.9%
65.86 1
 
1.9%
-44.86 1
 
1.9%
-9.71 1
 
1.9%
249.18 1
 
1.9%
258.23 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-3130.1 1
1.9%
-747.94 1
1.9%
-460.41 1
1.9%
-431.71 1
1.9%
-392.12 1
1.9%
-386.31 1
1.9%
-224.1 1
1.9%
-210.79 1
1.9%
-147.42 1
1.9%
-141.56 1
1.9%
ValueCountFrequency (%)
1001.46 1
1.9%
856.15 1
1.9%
453.92 1
1.9%
336.44 1
1.9%
330.47 1
1.9%
301.91 1
1.9%
284.79 1
1.9%
258.23 1
1.9%
253.74 1
1.9%
249.18 1
1.9%

Coke
Real number (ℝ)

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-41.085882
Minimum-427.38
Maximum225.73
Zeros0
Zeros (%)0.0%
Negative35
Negative (%)67.3%
Memory size548.0 B
2023-07-30T07:32:02.840899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-427.38
5-th percentile-203.235
Q1-80.075
median-28.28
Q310.71
95-th percentile108.24
Maximum225.73
Range653.11
Interquartile range (IQR)90.785

Descriptive statistics

Standard deviation106.35706
Coefficient of variation (CV)-2.5886522
Kurtosis4.2846004
Mean-41.085882
Median Absolute Deviation (MAD)46.32
Skewness-1.2274254
Sum-2095.38
Variance11311.824
MonotonicityNot monotonic
2023-07-30T07:32:03.224768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-24.83 1
 
1.9%
-84.15 1
 
1.9%
-56.88 1
 
1.9%
8.33 1
 
1.9%
-49.95 1
 
1.9%
18.04 1
 
1.9%
119.38 1
 
1.9%
-65.31 1
 
1.9%
43.55 1
 
1.9%
-132.98 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-427.38 1
1.9%
-366.12 1
1.9%
-223.69 1
1.9%
-182.78 1
1.9%
-160.71 1
1.9%
-132.98 1
1.9%
-117.94 1
1.9%
-117.25 1
1.9%
-115.87 1
1.9%
-92.5 1
1.9%
ValueCountFrequency (%)
225.73 1
1.9%
119.38 1
1.9%
110.26 1
1.9%
106.22 1
1.9%
56.45 1
1.9%
43.77 1
1.9%
43.55 1
1.9%
40 1
1.9%
26.9 1
1.9%
24.14 1
1.9%

Charcoal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Gases
Categorical

Distinct3
Distinct (%)75.0%
Missing48
Missing (%)92.3%
Memory size548.0 B
-2.57
-10.36
-8.56

Length

Max length6
Median length5
Mean length5.25
Min length5

Characters and Unicode

Total characters21
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row-10.36
2nd row-2.57
3rd row-2.57
4th row-8.56

Common Values

ValueCountFrequency (%)
-2.57 2
 
3.8%
-10.36 1
 
1.9%
-8.56 1
 
1.9%
(Missing) 48
92.3%

Length

2023-07-30T07:32:03.534377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:32:03.904099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.57 2
50.0%
10.36 1
25.0%
8.56 1
25.0%

Most occurring characters

ValueCountFrequency (%)
- 4
19.0%
. 4
19.0%
5 3
14.3%
2 2
9.5%
7 2
9.5%
6 2
9.5%
1 1
 
4.8%
0 1
 
4.8%
3 1
 
4.8%
8 1
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13
61.9%
Dash Punctuation 4
 
19.0%
Other Punctuation 4
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3
23.1%
2 2
15.4%
7 2
15.4%
6 2
15.4%
1 1
 
7.7%
0 1
 
7.7%
3 1
 
7.7%
8 1
 
7.7%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4
19.0%
. 4
19.0%
5 3
14.3%
2 2
9.5%
7 2
9.5%
6 2
9.5%
1 1
 
4.8%
0 1
 
4.8%
3 1
 
4.8%
8 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4
19.0%
. 4
19.0%
5 3
14.3%
2 2
9.5%
7 2
9.5%
6 2
9.5%
1 1
 
4.8%
0 1
 
4.8%
3 1
 
4.8%
8 1
 
4.8%

Other secondary
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16.505
Minimum-345.04
Maximum196.33
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)61.5%
Memory size548.0 B
2023-07-30T07:32:04.321744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-345.04
5-th percentile-154.5755
Q1-29.1075
median-7.265
Q314.765
95-th percentile61.594
Maximum196.33
Range541.37
Interquartile range (IQR)43.8725

Descriptive statistics

Standard deviation76.244175
Coefficient of variation (CV)-4.6194593
Kurtosis8.2751129
Mean-16.505
Median Absolute Deviation (MAD)22.245
Skewness-1.8964639
Sum-858.26
Variance5813.1743
MonotonicityNot monotonic
2023-07-30T07:32:04.801259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.98 2
 
3.8%
15.41 1
 
1.9%
-23.57 1
 
1.9%
-130.01 1
 
1.9%
-34.9 1
 
1.9%
-54.97 1
 
1.9%
196.33 1
 
1.9%
22.69 1
 
1.9%
23.57 1
 
1.9%
40.41 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-345.04 1
1.9%
-244.12 1
1.9%
-184.6 1
1.9%
-130.01 1
1.9%
-54.97 1
1.9%
-50.87 1
1.9%
-45.57 1
1.9%
-39.21 1
1.9%
-38.51 1
1.9%
-36.29 1
1.9%
ValueCountFrequency (%)
196.33 1
1.9%
78.38 1
1.9%
65.73 1
1.9%
58.21 1
1.9%
41.94 1
1.9%
40.41 1
1.9%
36.78 1
1.9%
23.57 1
1.9%
22.69 1
1.9%
22.37 1
1.9%

Non-energy
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.459808
Minimum-349.67
Maximum412.99
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)51.9%
Memory size548.0 B
2023-07-30T07:32:05.192957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-349.67
5-th percentile-202.86
Q1-54.1675
median-7.745
Q373.9275
95-th percentile326.978
Maximum412.99
Range762.66
Interquartile range (IQR)128.095

Descriptive statistics

Standard deviation159.83256
Coefficient of variation (CV)6.5344979
Kurtosis0.43162757
Mean24.459808
Median Absolute Deviation (MAD)72.02
Skewness0.54305513
Sum1271.91
Variance25546.448
MonotonicityNot monotonic
2023-07-30T07:32:05.460071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-202.86 2
 
3.8%
-53.93 1
 
1.9%
111.18 1
 
1.9%
-44.44 1
 
1.9%
20.06 1
 
1.9%
-349.67 1
 
1.9%
-34.01 1
 
1.9%
306.75 1
 
1.9%
-31.75 1
 
1.9%
28.23 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
-349.67 1
1.9%
-231.58 1
1.9%
-202.86 2
3.8%
-174.79 1
1.9%
-147.61 1
1.9%
-125.58 1
1.9%
-112.17 1
1.9%
-110.43 1
1.9%
-104.19 1
1.9%
-102.84 1
1.9%
ValueCountFrequency (%)
412.99 1
1.9%
383.5 1
1.9%
348.78 1
1.9%
309.14 1
1.9%
306.75 1
1.9%
267.28 1
1.9%
260.27 1
1.9%
246.77 1
1.9%
167.71 1
1.9%
166.8 1
1.9%

Total Secundaries
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-192.07327
Minimum-4212.44
Maximum1334.75
Zeros0
Zeros (%)0.0%
Negative32
Negative (%)61.5%
Memory size548.0 B
2023-07-30T07:32:05.706626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4212.44
5-th percentile-1065.329
Q1-667.9925
median-181.345
Q3205.3475
95-th percentile1284.189
Maximum1334.75
Range5547.19
Interquartile range (IQR)873.34

Descriptive statistics

Standard deviation877.91954
Coefficient of variation (CV)-4.5707534
Kurtosis7.6829758
Mean-192.07327
Median Absolute Deviation (MAD)418.555
Skewness-1.5171516
Sum-9987.81
Variance770742.72
MonotonicityNot monotonic
2023-07-30T07:32:05.950541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-345.28 1
 
1.9%
-411.99 1
 
1.9%
186.53 1
 
1.9%
320.23 1
 
1.9%
142.16 1
 
1.9%
-47.56 1
 
1.9%
1308.4 1
 
1.9%
-453.7 1
 
1.9%
444.67 1
 
1.9%
1088.87 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-4212.44 1
1.9%
-1267.95 1
1.9%
-1172.92 1
1.9%
-977.3 1
1.9%
-894.77 1
1.9%
-876.41 1
1.9%
-871.61 1
1.9%
-827.15 1
1.9%
-825.11 1
1.9%
-823.11 1
1.9%
ValueCountFrequency (%)
1334.75 1
1.9%
1322.72 1
1.9%
1308.4 1
1.9%
1264.38 1
1.9%
1088.87 1
1.9%
978.87 1
1.9%
961.87 1
1.9%
444.67 1
1.9%
411.42 1
1.9%
361.71 1
1.9%

Total
Real number (ℝ)

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-708.59038
Minimum-4574.51
Maximum2945.15
Zeros0
Zeros (%)0.0%
Negative37
Negative (%)71.2%
Memory size548.0 B
2023-07-30T07:32:06.207279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4574.51
5-th percentile-3270.676
Q1-1420.2475
median-805.42
Q3337.12
95-th percentile1368.4185
Maximum2945.15
Range7519.66
Interquartile range (IQR)1757.3675

Descriptive statistics

Standard deviation1518.1494
Coefficient of variation (CV)-2.1424923
Kurtosis0.37941358
Mean-708.59038
Median Absolute Deviation (MAD)892.33
Skewness-0.082684317
Sum-36846.7
Variance2304777.7
MonotonicityNot monotonic
2023-07-30T07:32:06.465926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-790.13 1
 
1.9%
-1348.83 1
 
1.9%
-739.5 1
 
1.9%
1215 1
 
1.9%
-987.87 1
 
1.9%
2945.15 1
 
1.9%
1030.54 1
 
1.9%
-854.06 1
 
1.9%
582.79 1
 
1.9%
263.67 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
-4574.51 1
1.9%
-3735.93 1
1.9%
-3652.09 1
1.9%
-2958.61 1
1.9%
-2801.41 1
1.9%
-2605.37 1
1.9%
-2439.29 1
1.9%
-2071.58 1
1.9%
-2069.11 1
1.9%
-2035.16 1
1.9%
ValueCountFrequency (%)
2945.15 1
1.9%
2606.88 1
1.9%
1391.26 1
1.9%
1349.73 1
1.9%
1215 1
1.9%
1030.54 1
1.9%
1011.24 1
1.9%
851.67 1
1.9%
840.75 1
1.9%
811.27 1
1.9%

Interactions

2023-07-30T07:31:48.245166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:46.078054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:51.563654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:55.167464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:59.826366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:03.306993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:06.331845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:10.090906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:14.311098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:17.772661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:21.498235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:26.192239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:29.625866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:33.028185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:37.103555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:41.168600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:44.733286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:48.754964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:46.581043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:51.962650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:55.580005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:00.270072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:03.518217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:06.482816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-30T07:30:54.372590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:59.050318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:02.545845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:05.643535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:08.956361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:13.514635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:17.018646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:20.668565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:25.430907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:28.846061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:32.268873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:35.987498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:40.391249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:43.927787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:47.472466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:52.494479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:50.538139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:54.565512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:59.257978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:02.726861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:05.823726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:09.209519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:13.699942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:17.195087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:20.862365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:25.611540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:29.026824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:32.458733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:36.278980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:40.578024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:44.107037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:47.653173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:52.696740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:50.873605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:54.775877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:59.473632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:02.930303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:05.995039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:09.544538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:13.925105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:17.410072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:21.075771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:25.815056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:29.244020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:32.655084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:36.594007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:40.785287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:44.313064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:47.849604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:54.981945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:51.210693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:54.965985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:30:59.651522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:03.113741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:06.173311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:09.785580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:14.113878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:17.579752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:21.281653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:25.992938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:29.433417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:32.829647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:36.835330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:40.982626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:44.530609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:31:48.042368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-30T07:31:55.325783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:31:55.932064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:31:56.302067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

171YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970-277.33NaN-167.52NaNNaNNaNNaNNaN-444.85NaN-97.67-18.35-54.86-42.01-69.03-24.83NaNNaN15.41-53.93-345.28-790.13
21971-966.96NaN30.12NaNNaNNaNNaNNaN-936.84NaN-104.43140.25-77.8214.57-210.79-84.15NaNNaN14.55-104.19-411.99-1348.83
31972-494.74NaN-88.54NaNNaNNaNNaNNaN-583.29NaN-105.66179.687.39-267.48-392.12-17.24NaNNaN58.21-202.86-740.09-1323.37
41973-3821.01NaN1.17NaNNaNNaNNaNNaN-3819.84NaN-61.43204.61-55.69-139.74301.91-4.83NaNNaN41.94-202.8683.91-3735.93
51974-1893.22NaN-279.55NaNNaNNaNNaNNaN-2172.77NaN-60.20-189.6168.78379.791001.46-2.07NaNNaN-39.21105.421264.38-908.39
61975899.12NaN-289.86NaNNaNNaNNaNNaN609.27NaN-63.89-150.67-108.89121.74205.2624.14NaNNaN-45.57260.27242.40851.67
71976427.91NaN-275.59NaNNaNNaNNaNNaN152.32NaN-129.00-461.12-54.86-102.88253.74-160.71NaNNaN19.21166.80-468.81-316.49
81977779.94NaN-412.92NaNNaNNaNNaNNaN367.02NaN-92.76-179.1011.46-173.18336.44-20.69NaNNaN65.7319.28-32.82334.20
91978972.74NaN-49.24NaNNaNNaNNaNNaN923.49NaN-174.46-332.74-73.72-314.64453.92-15.86NaNNaN-3.51348.78-112.22811.27
101979-2503.70NaN-306.57NaNNaNNaNNaNNaN-2810.27NaN-95.22-437.07-85.1847.15856.1540.00NaNNaN-8.76-112.17204.90-2605.37
171YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
4320122046.85-2693.07292.03NaNNaNNaNNaN30.68-323.52NaN-59.57815.2621.22319.1870.89225.73NaNNaN-26.50-31.461334.751011.24
4420131115.55-2675.45-194.94NaNNaNNaNNaN-27.74-1782.58NaN4.37926.1832.89-129.4033.8623.75NaNNaN5.9564.29961.87-820.71
452014756.87-2256.20-140.85NaNNaNNaNNaN-24.23-1664.41NaN-10.08-412.33-55.51-179.45-53.09-67.47NaNNaN-1.624.66-774.88-2439.29
462015-502.24-1840.16-280.31NaNNaNNaNNaN8.12-2614.59NaN-1.78959.8725.25312.98-26.9143.77NaNNaN-24.4934.021322.72-1291.87
472016-508.69NaN-371.53NaNNaNNaNNaNNaN-880.22NaN45.60226.4048.49-212.78-1.51-15.96NaNNaN-7.5564.26146.94-733.28
482017228.32NaN-156.37NaNNaNNaNNaNNaN71.95NaN-67.18-223.08-62.48-94.31-64.18-77.56NaNNaN4.71-10.63-594.71-522.76
492018-156.32NaN-221.53NaNNaNNaNNaNNaN-377.85NaN-94.60-876.29204.97150.86214.49-366.12NaNNaN-21.03-35.40-823.11-1200.96
502019-1744.95NaN11.58NaNNaNNaNNaNNaN-1733.37NaN19.26-27.07-83.94-190.89-34.59-32.10NaNNaN-28.6339.75-338.21-2071.58
5120201456.00-3618.96182.45NaNNaNNaNNaNNaN-1980.51NaN14.78-102.1330.1825.14-63.94110.26NaNNaN-50.87-18.07-54.65-2035.16
522021-1007.78-3598.9367.82NaNNaNNaNNaNNaN-4538.89NaN-63.68661.00-52.57-22.66-431.71-92.50NaNNaN-25.31-8.20-35.62-4574.51